IP Library › Granted Patent US 12,530,882
Granted Patent B2
US 12,530,882 · App. 17/839,331 · Granted Jan 20, 2026

Efficient artificial intelligence-based base calling of index sequences

Inventors: Anindita Dutta (San Francisco, CA); Gery Vessere (Oakland, CA)
Assignee: Illumina, Inc.
G06V10/82G06T7/38G06T7/73G06V10/75G06T2207/20084
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Quick Facts
Patent No.
US 12,530,882
App. No.
17/839,331
Filed
Jun 13, 2022
Granted
Jan 20, 2026
Kind
B2
Art Unit
2662
USPC
382/155
Abstract

Techniques for improving artificial intelligence-based base calling are disclosed. The improved techniques can be used to better train artificial intelligence for base calling by reordering of sequencing images, and training of a neural network-based base caller where the temporal logic is effectively “frozen” (or bypassed). In addition, the improved techniques include various combinations, including, for example, combining “normalization” of sequencing images with reordering of sequencing images and/or with effectively “freezing” the temporal logic.

Claims (46)

1 . A computer-implemented method, including:

accessing a set of sequencing images depicting emission signals from nucleotide clusters, wherein sequencing images in the set of sequencing images are arranged in a first order;

reordering the sequencing images depicting emission signals in a reordered set of the sequencing images,

wherein the sequencing images in the reordered set of the sequencing images are arranged in a second order, and

wherein the first order is different than the second order; and

processing the reordered set of the sequencing images and generating at least one base call prediction for a nucleotide base in a sequence read based on the reordered set of the sequencing images.

2 . The computer-implemented method of claim 1 , further including training a neural network-based base caller over a plurality of training iterations, wherein respective training iterations in the plurality of training iterations process respective reordered sets of the sequencing images through the neural network-based base caller to generate respective base call predictions, and update weights of the neural network-based base caller based on comparison of the respective base call predictions against respective base call ground truths.

3 . The computer-implemented method of claim 2 , wherein the sequencing images are arranged at a center position, at one or more right flanking positions, and at one or more left flanking positions.

4 . The computer-implemented method of claim 3 , wherein a particular one of the sequencing images is kept fixed at the center position between the first order and the second order.

5 . The computer-implemented method of claim 4 , wherein at least some of the sequencing images at the one or more right flanking positions and the one or more left flanking positions are permutated between the first order and the second order.

6 . The computer-implemented method of claim 5 , wherein at least some of the sequencing images at the one or more right flanking positions and the one or more left flanking positions are kept fixed between the first order and the second order.

7 . The computer-implemented method of claim 6 , further including training the neural network-based base caller over a first training iteration that processes the set of sequencing images through the neural network-based base caller to generate a first base call prediction, and updates weights of the neural network-based base caller based on comparison of the first base call prediction against a first base call ground truth.

8 . The computer-implemented method of claim 7 , further including training the neural network-based base caller over a second training iteration that processes the reordered set of sequencing images through the neural network-based base caller to generate a second base call prediction, and updates weights of the neural network-based base caller based on comparison of the second base call prediction against a second base call ground truth.

9 . The computer-implemented method of claim 1 , wherein respective ones of the sequencing images are generated for respective sequencing cycles of a sequencing run in the first order.

10 . The computer-implemented method of claim 9 , wherein respective ones of the sequencing images are respective index images generated for respective index sequencing cycles of the sequencing run in the first order.

11 . The computer-implemented method of claim 1 , further including:

selecting a plurality of subsets of N sequencing images from a set of M sequencing images, wherein respective subsets in the plurality of subsets arrange the N sequencing images in respective sequences of the N sequencing images; and wherein M>N.

12 . The computer-implemented method of claim 11 , wherein the respective sequences have different combinations of the N sequencing images.

13 . The computer-implemented method of claim 12 , wherein the different combinations are combinations with replacement.

14 . The computer-implemented method of claim 12 , wherein the different combinations are combinations without replacement.

15 . The computer-implemented method of claim 11 , wherein the respective sequences have different permutations of the N sequencing images.

16 . The computer-implemented method of claim 15 , wherein the different permutations are permutations with repetition.

17 . The computer-implemented method of claim 15 , wherein the different permutations are permutations without repetition.

18 . The computer-implemented method of claim 11 , wherein the respective sequences arrange the N sequencing images in accordance with consecutive sequencing cycles.

19 . The computer-implemented method of claim 11 , wherein the respective sequences arrange the N sequencing images in accordance with non-consecutive sequencing cycles.

20 . The computer-implemented method of claim 11 , wherein the respective sequences arrange the N sequencing images in accordance with a combination of consecutive sequencing cycles and non-consecutive sequencing cycles.

21 . The computer-implemented method of claim 11 , wherein N for the N sequencing images varies between the respective sequences.

22 . The computer-implemented method of claim 11 , wherein N for the N sequencing images is constant between the respective sequences.

23 . The computer-implemented method of claim 11 , wherein the respective sequences have overlapping sequencing images.

24 . The computer-implemented method of claim 11 , wherein the respective sequences have no overlapping sequencing images.

25 . The computer-implemented method of claim 11 , further including training a neural network-based base caller over respective training iterations that process the respective sequences of the N sequencing images through the neural network-based base caller to generate respective base call predictions, and update weights of the neural network-based base caller based on comparison of the respective base call predictions against respective base call ground truths.

26 . The computer-implemented method of claim 11 , wherein the set of M sequencing images are generated for consecutive sequencing cycles.

27 . The computer-implemented method of claim 11 , wherein the set of M sequencing images are generated for non-consecutive sequencing cycles.

28 . The computer-implemented method of claim 11 , wherein the set of M sequencing images are generated for a combination of consecutive sequencing cycles and non-consecutive sequencing cycles.

29 . A computer-implemented method, including:

accessing a plurality of sequences of N sequencing images; and

training a neural network-based base caller over a plurality of training iterations, wherein respective training iterations in the plurality of training iterations process respective sequences of the N sequencing images in the plurality of sequences in respective orders through the neural network-based base caller to generate respective base call predictions,

wherein a first order for the N sequencing images of the respective orders differs from a second order for the N sequencing images of the respective orders, and

wherein the respective training iterations in the plurality of training iterations update weights of the neural network-based base caller based on comparison of the respective base call predictions against respective base call ground truths.

30 . The computer-implemented method of claim 29 , wherein the respective sequences arrange the N sequencing images at a center position, at one or more right flanking positions, and at one or more left flanking positions.

31 . A system, comprising:

accessing logic configured to access a set of sequencing images depicting emission signals from nucleotide clusters, wherein sequencing images in the set of sequencing images are arranged in a first order;

reordering logic configured to reorder the sequencing images depicting emission signals in a reordered set of the sequencing images,

wherein the sequencing images in the reordered set of the sequencing images are arranged in a second order, and

wherein the first order is different than the second order; and

processing logic configured to process the reordered set of the sequencing images and generate at least one base call prediction for a nucleotide base in a sequence read based on the reordered set of the sequencing images.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 17, 2023
From: DUTTA, ANINDITA; VESSERE, GERY
To: ILLUMINA, INC.
Reel/Frame 062391/0734 →
Continuity (2)
Provisional Application 63217644 · Jul 1, 2021
Related Publication 20230005253A1 · Jan 5, 2023
References Cited (400)
US 5641658A · Adams et al. · 1997 [cited by applicant]
US 6090592A · Adams et al. · 2000 [cited by applicant]
US 7057026B2 · Barnes et al. · 2006 [cited by applicant]
US 7115400B1 · Adessi et al. · 2006 [cited by applicant]
US 7211414B2 · Hardin et al. · 2007 [cited by applicant]
US 7315019B2 · Turner et al. · 2008 [cited by applicant]
US 7329492B2 · Hardin et al. · 2008 [cited by applicant]
US 7405281B2 · Xu et al. · 2008 [cited by applicant]
US 7427673B2 · Balasubramanian et al. · 2008 [cited by applicant]
US 7541444B2 · Milton et al. · 2009 [cited by applicant]
US 7566537B2 · Balasubramanian et al. · 2009 [cited by applicant]
US 7592435B2 · Milton et al. · 2009 [cited by applicant]
US 8182993B2 · Tomaney et al. · 2012 [cited by applicant]
US 8241573B2 · Banerjee et al. · 2012 [cited by applicant]
US 8392126B2 · Mann · 2013 [cited by applicant]
US 8401258B2 · Hargrove et al. · 2013 [cited by applicant]
US 8407012B2 · Erlich et al. · 2013 [cited by applicant]
US 8594439B2 · Staelin et al. · 2013 [cited by applicant]
US 8725425B2 · Heiner et al. · 2014 [cited by applicant]
US 8795971B2 · Kersey et al. · 2014 [cited by applicant]
US 8965076B2 · Garcia et al. · 2015 [cited by applicant]
US 9279154B2 · Previte et al. · 2016 [cited by applicant]
US 9453258B2 · Kain et al. · 2016 [cited by applicant]
US 9708656B2 · Turner et al. · 2017 [cited by applicant]
US 10023911B2 · Tomaney et al. · 2018 [cited by applicant]
US 10068054B2 · Van Rooyen et al. · 2018 [cited by applicant]
US 10152776B2 · Langlois et al. · 2018 [cited by applicant]
US 10168438B2 · Dennis et al. · 2019 [cited by applicant]
US 10241075B2 · Davey et al. · 2019 [cited by applicant]
US 10354747B1 · DePristo et al. · 2019 [cited by applicant]
US 10423861B2 · Gao et al. · 2019 [cited by applicant]
US 10491239B1 · Hubara · 2019 [cited by applicant]
US 10527549B2 · Rebetez et al. · 2020 [cited by applicant]
US 10540591B2 · Gao et al. · 2020 [cited by applicant]
US 10619195B2 · Lamb et al. · 2020 [cited by applicant]
US 10648027B2 · Mannion et al. · 2020 [cited by applicant]
US 10711299B2 · Rothberg et al. · 2020 [cited by applicant]
US 10713794B1 · He et al. · 2020 [cited by applicant]
US 10740880B2 · Paik et al. · 2020 [cited by applicant]
US 10740883B2 · Zerfass et al. · 2020 [cited by applicant]
US 10755810B2 · Buckler et al. · 2020 [cited by applicant]
US 10963673B2 · Schaumberg et al. · 2021 [cited by applicant]
US 11138496B2 · Seth · 2021 [cited by applicant]
US 20020055100A1 · Kawashima et al. · 2002 [cited by applicant]
US 20030062485A1 · Fernandez et al. · 2003 [cited by applicant]
US 20040002090A1 · Mayer et al. · 2004 [cited by applicant]
US 20040096853A1 · Mayer · 2004 [cited by applicant]
US 20060014151A1 · Ogura et al. · 2006 [cited by applicant]
US 20060040297A1 · Leamon et al. · 2006 [cited by applicant]
US 20060064248A1 · Saidi et al. · 2006 [cited by applicant]
US 20060188901A1 · Barnes et al. · 2006 [cited by applicant]
US 20060240439A1 · Smith et al. · 2006 [cited by applicant]
US 20060269130A1 · Maroy et al. · 2006 [cited by applicant]
US 20070128624A1 · Gormley et al. · 2007 [cited by applicant]
US 20070166705A1 · Milton et al. · 2007 [cited by applicant]
US 20080009420A1 · Schroth et al. · 2008 [cited by applicant]
US 20080234136A1 · Drmanac et al. · 2008 [cited by applicant]
US 20080242560A1 · Gunderson et al. · 2008 [cited by applicant]
US 20090081775A1 · Hodneland et al. · 2009 [cited by applicant]
US 20100046830A1 · Wang et al. · 2010 [cited by applicant]
US 20100111370A1 · Black et al. · 2010 [cited by applicant]
US 20100157086A1 · Segale et al. · 2010 [cited by applicant]
US 20110059865A1 · Smith et al. · 2011 [cited by applicant]
US 20110065607A1 · Kersey et al. · 2011 [cited by applicant]
US 20110281736A1 · Drmanac et al. · 2011 [cited by applicant]
US 20110286628A1 · Goncalves et al. · 2011 [cited by applicant]
US 20110295902A1 · Mande et al. · 2011 [cited by applicant]
US 20120015825A1 · Zhong et al. · 2012 [cited by applicant]
US 20120020537A1 · Garcia et al. · 2012 [cited by applicant]
US 20130059740A1 · Drmanac et al. · 2013 [cited by applicant]
US 20130079232A1 · Kain et al. · 2013 [cited by applicant]
US 20130124100A1 · Drmanac et al. · 2013 [cited by applicant]
US 20130188866A1 · Obrador et al. · 2013 [cited by applicant]
US 20130250407A1 · Schaffer et al. · 2013 [cited by applicant]
US 20140051588A9 · Drmanac et al. · 2014 [cited by applicant]
US 20140152801A1 · Fine et al. · 2014 [cited by applicant]
US 20150079596A1 · Eltoukhy et al. · 2015 [cited by applicant]
US 20150117784A1 · Lin et al. · 2015 [cited by applicant]
US 20150169824A1 · Kermani et al. · 2015 [cited by applicant]
US 20160042511A1 · Chukka et al. · 2016 [cited by applicant]
US 20160078272A1 · Hammoud · 2016 [cited by applicant]
US 20160110498A1 · Bruand et al. · 2016 [cited by applicant]
US 20160196479A1 · Chertok et al. · 2016 [cited by applicant]
US 20160350914A1 · Champlin et al. · 2016 [cited by applicant]
US 20160356715A1 · Zhong et al. · 2016 [cited by applicant]
US 20160357903A1 · Shendure et al. · 2016 [cited by applicant]
US 20160371431A1 · Haque et al. · 2016 [cited by applicant]
US 20170044601A1 · Crnogorac et al. · 2017 [cited by applicant]
US 20170098032A1 · Desai et al. · 2017 [cited by applicant]
US 20170116520A1 · Min et al. · 2017 [cited by applicant]
US 20170161545A1 · Champlin et al. · 2017 [cited by applicant]
US 20170169313A1 · Choi et al. · 2017 [cited by applicant]
US 20170249421A1 · Eberle et al. · 2017 [cited by applicant]
US 20170249744A1 · Wang et al. · 2017 [cited by applicant]
US 20170362634A1 · Ota et al. · 2017 [cited by applicant]
US 20180075279A1 · Gertych et al. · 2018 [cited by applicant]
US 20180107927A1 · Frey · 2018 [cited by applicant]
US 20180114337A1 · Li et al. · 2018 [cited by applicant]
US 20180189613A1 · Wolf et al. · 2018 [cited by applicant]
US 20180195953A1 · Langlois et al. · 2018 [cited by applicant]
US 20180201992A1 · Wu et al. · 2018 [cited by applicant]
US 20180211001A1 · Gopalan et al. · 2018 [cited by applicant]
US 20180240032A1 · van Rooyen · 2018 [cited by examiner]
US 20180274023A1 · Belitz et al. · 2018 [cited by applicant]
US 20180305751A1 · Vermaas et al. · 2018 [cited by applicant]
US 20180322327A1 · Smith et al. · 2018 [cited by applicant]
US 20180330824A1 · Athey · 2018 [cited by applicant]
US 20180334711A1 · Kelley et al. · 2018 [cited by applicant]
US 20180334712A1 · Singer et al. · 2018 [cited by applicant]
US 20180340234A1 · Scafe et al. · 2018 [cited by applicant]
US 20190034586A1 · Pirrotte et al. · 2019 [cited by applicant]
US 20190080450A1 · Arar et al. · 2019 [cited by applicant]
US 20190107642A1 · Farhadi Nia et al. · 2019 [cited by applicant]
US 20190114544A1 · Sundaram et al. · 2019 [cited by applicant]
US 20190156915A1 · Zhang et al. · 2019 [cited by applicant]
US 20190164010A1 · Ma et al. · 2019 [cited by applicant]
US 20190170680A1 · Sikora et al. · 2019 [cited by applicant]
US 20190180153A1 · Buckler et al. · 2019 [cited by applicant]
US 20190213473A1 · Dutta · 2019 [cited by examiner]
US 20190237160A1 · Rothberg et al. · 2019 [cited by applicant]
US 20190237163A1 · Wang et al. · 2019 [cited by applicant]
US 20190244348A1 · Buckler et al. · 2019 [cited by applicant]
US 20190266491A1 · Gao et al. · 2019 [cited by applicant]
US 20190272638A1 · Mouton et al. · 2019 [cited by applicant]
US 20190332118A1 · Wang et al. · 2019 [cited by applicant]
US 20190392578A1 · Chukka et al. · 2019 [cited by applicant]
US 20200019859A1 · Farh · 2020 [cited by examiner]
US 20200027002A1 · Hickson et al. · 2020 [cited by applicant]
US 20200054306A1 · Mehanian et al. · 2020 [cited by applicant]
US 20200057838A1 · Yekhanin et al. · 2020 [cited by applicant]
US 20200065675A1 · Sundaram et al. · 2020 [cited by applicant]
US 20200125947A1 · Park et al. · 2020 [cited by applicant]
US 20200176082A1 · Massingham · 2020 [cited by applicant]
US 20200193597A1 · Fan et al. · 2020 [cited by applicant]
US 20200226368A1 · Bakalo et al. · 2020 [cited by applicant]
US 20200256856A1 · Chou et al. · 2020 [cited by applicant]
US 20200302223A1 · Dutta et al. · 2020 [cited by applicant]
US 20200302224A1 · Jaganathan et al. · 2020 [cited by applicant]
US 20200302297A1 · Jaganathan et al. · 2020 [cited by applicant]
US 20200302603A1 · Barnes et al. · 2020 [cited by applicant]
US 20200320294A1 · Mangal et al. · 2020 [cited by applicant]
US 20200327377A1 · Jaganathan et al. · 2020 [cited by applicant]
US 20200342955A1 · Guo et al. · 2020 [cited by applicant]
US 20200364565A1 · Kostem · 2020 [cited by applicant]
US 20200388029A1 · Saltz et al. · 2020 [cited by applicant]
US 20210027462A1 · Bredno et al. · 2021 [cited by applicant]
US 20210056287A1 · Schaumburg et al. · 2021 [cited by applicant]
US 20210072391A1 · Li et al. · 2021 [cited by applicant]
US 20210089827A1 · Kumagai et al. · 2021 [cited by applicant]
US 20210115490A1 · Embree et al. · 2021 [cited by applicant]
US 20210390278A1 · Van Leeuwen et al. · 2021 [cited by applicant]
CA 2894317A1 · 2016 [cited by applicant]
CA 3104851A1 · 2020 [cited by applicant]
CN 110245685A · 2019 [cited by applicant]
EP 3130681A1 · 2017 [cited by applicant]
EP 3373238A1 · 2018 [cited by applicant]
JP 2007199397A · 2007 [cited by applicant]
WO 9106678A1 · 1991 [cited by applicant]
WO 2004018497A2 · 2004 [cited by applicant]
WO 2005065814A1 · 2005 [cited by applicant]
WO 2006064199A1 · 2006 [cited by applicant]
WO 2007010251A2 · 2007 [cited by applicant]
WO 2007123744A2 · 2007 [cited by applicant]
WO 2008154317A1 · 2008 [cited by applicant]
WO 2012058096A1 · 2012 [cited by applicant]
WO 2014142921A1 · 2014 [cited by applicant]
WO 2015084985A2 · 2015 [cited by applicant]
WO 2016145516A1 · 2016 [cited by applicant]
WO 2016201564A1 · 2016 [cited by applicant]
WO 2017184997A1 · 2017 [cited by applicant]
WO 2018129314A1 · 2018 [cited by applicant]
WO 2018165099A1 · 2018 [cited by applicant]
WO 2018203084A1 · 2018 [cited by applicant]
WO 2019027767A1 · 2019 [cited by applicant]
WO 2019028047A1 · 2019 [cited by applicant]
WO 2019055856A1 · 2019 [cited by applicant]
WO 2019079182A1 · 2019 [cited by applicant]
WO 2019079202A1 · 2019 [cited by applicant]
WO 2019090251A2 · 2019 [cited by applicant]
WO 2019136284A1 · 2019 [cited by applicant]
WO 2019136388A1 · 2019 [cited by applicant]
WO 2019140402A1 · 2019 [cited by applicant]
WO 2019147904A1 · 2019 [cited by applicant]
WO 2020014280A1 · 2020 [cited by applicant]
WO PCTUS2020024087 · 2020 [cited by applicant]
WO PCTUS2020024088 · 2020 [cited by applicant]
WO PCTUS2020024090 · 2020 [cited by applicant]
WO PCTUS2020024091 · 2020 [cited by applicant]
WO PCTUS2020024092 · 2020 [cited by applicant]
WO PCTUS2020033280 · 2020 [cited by applicant]
WO PCTUS2020033281 · 2020 [cited by applicant]
WO 2020123552A1 · 2020 [cited by applicant]
WO PCTUS2021018258 · 2021 [cited by applicant]
WO PCTUS2021018422 · 2021 [cited by applicant]
WO PCTUS2021018427 · 2021 [cited by applicant]
WO PCTUS2021018910 · 2021 [cited by applicant]
WO PCTUS2021018913 · 2021 [cited by applicant]
WO PCTUS2021018915 · 2021 [cited by applicant]
WO PCTUS2021018917 · 2021 [cited by applicant]
WO PCTUS2021047763 · 2021 [cited by applicant]
WO PCTUS2022020460 · 2022 [cited by applicant]
WO PCTUS2022020462 · 2022 [cited by applicant]
WO PCTUS2022021814 · 2022 [cited by applicant]
WO PCTUS202224911 · 2022 [cited by applicant]
WO PCTUS202224913 · 2022 [cited by applicant]
WO PCTUS202224916 · 2022 [cited by applicant]
WO PCTUS202224918 · 2022 [cited by applicant]
WO PCTUS2022035564 · 2022 [cited by applicant]
WO PCTUS2022035567 · 2022 [cited by applicant]
WO PCTUS2022035847 · 2022 [cited by applicant]
Semantic Segmentation Examples—MATLAB and Simulink, 22 pages, [retrieved on Jul. 21, 2021], Retrieved from the internet [URL: https://www.mathworks.com/help/vision/ug/semantic-segmentation-examples.html ]. [cited by applicant]
Bentley et al., Accurate Whole Human Genome Sequencing using Reversible Terminator Chemistry, Supplemental Information, Nature, dated Nov. 6, 2008, 55 pages, [retrieved on Jul. 21, 2021], retrieved from the internet [UR… [cited by applicant]
Illumina, GA Bootcamp, Sequencing Module 3: Overview, Broad Institute, 73 pages, [retrieved on Jul. 22, 2021]. [cited by applicant]
Grange, NGS: the basics, Institut Jacques Monod, dated Jun. 26, 2000, 59 pages. [cited by applicant]
Massingham et. al., All Your Base: a fast and accurate probabilistic approach to base calling, European Bioinformatics Institute, 22 pages, [retrieved on Jul. 22, 2021], Retrieved from the internet [URL: https://www.ebi… [cited by applicant]
Krishnakumar et al., Systematic and stochastic influences on the performance of the MinION nanopore sequencer across a range of nucleotide bias, Scientific Reports, published Feb. 16, 2018, 13 pages. [cited by applicant]
Tegfalk, Application of Machine Learning techniques to perform base-calling in next-generation DNA sequencing, KTH Royal Institue of Technology, dated 2020, 53 pages. [cited by applicant]
Kircher et al., Improved base-calling for the Iillumina Genome Analyzer using Machine Learning Strategies, Genome Biology 2009, I O:R83, Aug. 14, 2009, 10 pages. [cited by applicant]
Adriana Romero et al., FitNets: Hints for Thin Deep Nets, published Mar. 27, 2015, 13 pages. [cited by applicant]
Robinson et al., Computational Exome and Genome Analysis—Chapter 3 Illumina Technology, dated 2018, 25 pages. [cited by applicant]
Pfeiffer et. al., Systematic evaluation of error rates and causes in short samples in next-generation sequencing, Scientific Reports, published Jul. 19, 2018, 14 pages. [cited by applicant]
Puckelwartz et al., Supercomputing for the parallelization of whole genome analysis, Bioinformatics, dated Feb. 12, 2014, pp. 1508-1513, 6 pages. [cited by applicant]
Kelly et al., Churchill: an ultra-fast, deterministic, highly scalable and balanced parallelization strategy for the discovery of human genetic variation in clinical and population-scale genomics, Genome Biology, Bio-Me… [cited by applicant]
Wang et al., Achieving Accurate and Fast Base-calling by a Block model of the Illumina Sequencing Data, Science Direct, vol. 48, No. 28, dated Jan. 1, 2015, pp. 1462-1465, 4 pages. [cited by applicant]
Gao et al., Deep Learning in Protein Structural Modeling and Design, Patterns—CelPress, dated Dec. 11, 2020, 23 pages. [cited by applicant]
Pejaver et al., Inferring the molecular and phenotypic impact of amino acid variants with MutPred2—with Supplementary Information, Nature Communications, dated 2020, 59 pages. [cited by applicant]
Pakhrin et al., Deep learning based advances in protein structure prediction, International Journal of Molecular sciences, published May 24, 2021, 30 pages. [cited by applicant]
Wang et al. Predicting the impacts of mutations on protein-ligand binding affinity based on molecular dynamics simulations and machine learning methods, Computational and Structural Biotechnology Journal 18, dated Feb. … [cited by applicant]
Iqbal et al., Comprehensive characterization of amino acid positions in protein structures reveals molecular effects of missense variants, and supplemental information, PNAS, vol. 117, No. 45, dated Nov. 10, 2020, 35 pa… [cited by applicant]
Forghani et al., Convolutional Neural Network Based Approach to In Silica Non-Anticipating Prediction of Antigenic Distance for Influenza Virus, Viruses, published Sep. 12, 2020, vol. 12, 20 pages. [cited by applicant]
Jing et al., Learning from protein structure with geometric vector perceptrons, Arxiv: 2009: 01411v2, dated Dec. 31, 2020, 18 pages. [cited by applicant]
Hacteria Wiki, HiSeq2000—Next Level Hacking—Hackteria Wiki, retrieved on Apr. 12, 2021, retrieved from the internet [URL: https://www.hackteria.org/wiki/HiSeq2000_-_Next_Level_Hacking ], 42 pages. [cited by applicant]
Pei et al., A Topological Measurement for Weighted Protein Interaction Network, IEEE Computational Systems Bioinformatics Conference dated 2005, 11 pages. [cited by applicant]
Assfalg et. al., “3DString, A Feature String Kernel for 3D Object Classification on Voxelized Data”, dated Nov. 6, 2006, 10 pages. [cited by applicant]
Sanders, S. J. et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485, 237-241 (2012). [cited by applicant]
De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209-215 (2014). [cited by applicant]
Deciphering Developmental Disorders Study. Large-scale discovery of novel genetic causes of developmental disorders. Nature 519, 223-228 (2015). [cited by applicant]
Deciphering Developmental Disorders Study. Prevalence and architecture of de novo mutations in developmental disorders. Nature 542, 433-438 (2017). [cited by applicant]
Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216-221 (2014). [cited by applicant]
Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996-1006 (2002). [cited by applicant]
Tyner, C. et al. The UCSC Genome Browser database—2017 update. Nucleic Acids Res. 45, D626-D634 (2017). [cited by applicant]
Kabsch, W., & Sander, C. Dictionary of protein secondary structure—pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22, 2577-2637 (1983). [cited by applicant]
Joosten, R. P. et al. A series of PDB related databases for everyday needs. Nucleic Acids Res. 39, 411-419 (2011). [cited by applicant]
Tonita-Laza, I., McCallum, K., Xu, B., & Buxbaum, J. D. A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat. Genet. 48, 214-220 (2016). [cited by applicant]
Li, B. et al. Automated inference of molecular mechanisms of disease from amino acid substitutions. Bioinformatics 25, 2744-2750 (2009). [cited by applicant]
Shihab, H. A. et al. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Human. Mutat. 34, 57-65 (2013). [cited by applicant]
Liu, X., Wu, C., Li, C., & Boerwinkle, E. dbNSFPv3.0 a one-stop database of functional predictions and annotations for human nonsynonymous and splice-site SNVs. Human. Mutat. 37, 235-241 (2016). [cited by applicant]
Jain, S., White, M., Radivojac, P. Recovering true classifier performance in positive-unlabeled learning. in Proceedings Thirty-First AAAI Conference on Artificial Intelligence. 2066-2072 (AAAI Press, San Francisco; 201… [cited by applicant]
De Ligt, J. et al. Diagnostic exome sequencing in persons with severe intellectual disability. N. Engl. J. Med. 367, 1921-1929 (2012). [cited by applicant]
Iossifov, I. et al. De novo gene disruptions in children on the autistic spectrum. Neuron 74, 285-299 (2012). [cited by applicant]
O'Roak, B. J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246-250 (2012). [cited by applicant]
Rauch, A. et al. Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability—an exome sequencing study. Lancet 380, 1674-1682 (2012). [cited by applicant]
Epi, K. C. et al. De novo mutations in epileptic encephalopathies. Nature 501, 217-221 (2013). [cited by applicant]
EuroEPINOMICS-RES Consortium, Epilepsy Phenome/Genome Project, Epi4K Consortium. De novo mutations in synaptic transmission genes including DNM1 cause epileptic encephalopathies. Am. J. Hum. Genet. 95, 360-370 (2014). [cited by applicant]
Gilissen, C. et al. Genome sequencing identifies major causes of severe intellectual disability. Nature 511, 344-347 (2014). [cited by applicant]
Lelieveld, S. H. et al. Meta-analysis of 2, 104 trios provides support for 10 new genes for intellectual disability. Nat. Neurosci. 19, 1194-1196 (2016). [cited by applicant]
Famiglietti, M. L. et al. Genetic variations and diseases in UniProtKB Swiss-Prot—the ins and outs of expert manual curation. Human. Mutat. 35, 927-935 (2014). [cited by applicant]
Horaitis, O., Talbot, C. C.Jr., Phommarinh, M., Phillips, K. M., & Cotton, R. G. A database of locus-specific databases. Nat. Genet. 39, 425 (2007). [cited by applicant]
Stenson, P. D. et al. The Human Gene Mutation Database—building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum. Genet. 133, 1-9 (2014). [cited by applicant]
Alipanahi, et al., “Predicting the Sequence Specificities of DNA and RNA Binding Proteins by Deep Learning”, Aug. 2015, 9pgs. [cited by applicant]
Leung, et. al., “Deep learning of the tissue regulated splicing code”, 2014, 9pgs. [cited by applicant]
Park, et. al., “Deep Learning for Regulatory Genomics”, Aug. 2015, 2pgs. [cited by applicant]
Sundaram, et. al., “Predicting the clinical impact of human mutation with deep neural networks”, Aug. 2018, 15pgs. [cited by applicant]
Torng, Wen, et al., “3D deep convolutional neural networks for amino acid environment similarity analysis”, 2017, 23pages. [cited by applicant]
Grob, C., et. al., “Predicting variant deleteriousness in non human species Applying the CADD approach in mouse”, 2018, 11 pages. [cited by applicant]
Alberts, Bruce, et al., “Molecular biology of the cell”, Sixth Edition, 2015, 3 pages. [cited by applicant]
Illumina, “Indexed Sequencing Overview Guide”, Document No. 15057455, v. 5, Mar. 2019. [cited by applicant]
Ramesh, Nisha, et. al., “Cell Segmentation Using a Similarity Interface With a Multi-Task Convolutional Neural Network”; IEEE Journal of Biomedical and Health Informatics, vol. 23, No. 4, Jul. 2019, 12 pages. [cited by applicant]
Pu et. al., “DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network”, dated Feb. 4, 2019, 23 pages. [cited by applicant]
Adam, “Deep learning, 3D technology to improve structure modeling for protein interactions, create better drugs”, dated Jan. 9, 2020, 4 pages. [cited by applicant]
Varela, “Ligvoxel: A Deep Learning Pharmacore-Field Predictor”, dated Mar. 19, 2019, 5 pages. [cited by applicant]
Li et. al., “Predicting changes in protein thermostability upon mutation with deep 3D convolutional neural networks”, dated Feb. 28, 2020, 21 pages. [cited by applicant]
Raschka et. al., “Machine Learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition”, dated Jun. 6, 2020, 33 pages. [cited by applicant]
Morrone et. al., “Combining docking pose rank and structure with deep learning improves protein-ligand binding mode prediction”, dated Oct. 7, 2019, 13 pages. [cited by applicant]
Li, “Machine Learning Methods for Medical and Biological Image Computing”, dated Summer 2016, 113 pages. [cited by applicant]
Rivera et. al., “A Deep Learning Approach to Protein Structure Prediction”, dated Apr. 24, 2019, 22 pages. [cited by applicant]
Aritake et. al., “Single-molecule localization by voxel-wise regression using convolutional neural network”, dated Nov. 3, 2020, 11 pages. [cited by applicant]
Townshend et. al., “End-to-End Learning on 3D Protein Structure for Interface Prediction”, dated 2019, 10 pages. [cited by applicant]
Amidi et. al., “EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation”, dated Jul. 25, 2017, 18 pages. [cited by applicant]
Luna, “Machine Learning in structural biology and chemoinformatics”, dated 2019, 106 pages. [cited by applicant]
Anonymous, “Transferrable end-to-end learning for protein interface prediction”, dated 2019, 12 pages. [cited by applicant]
Dias et. al., “Artificial intelligence in clinical and genomic diagnostics”, dated 2019, 12 pages. [cited by applicant]
Luna et. al., “A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection”, dated May 27, 2020, 12 pages. [cited by applicant]
Li et. al., “DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction”, dated 2019, 8 pages. [cited by applicant]
Zhang et. al., “Template-based prediction of protein structure with deep learning”, dated Jun. 2, 2020, 16 pages. [cited by applicant]
Jaganathan, K. et. al., “Predicting splicing from primary sequence with deep learning”, Cell 176, 535-548, (2019). [cited by applicant]
Kircher, Martin, et al. “A general framework for estimating the relative pathogenicity of human genetic variants.” Nature genetics 46.3 (2014): 310. (Year:2014). [cited by applicant]
LeCun, Y., Botlou, L., Bengio, Y., & Haffner, P. Gradient based learning applied to document recognition. Proc. IEEE 86, 2278-2324 (1998). [cited by applicant]
Zhu, X. Need, A. C., Petrovski, S. & Goldstein, D. B. One gene, many neuropsychiatric disorders: lessons from Mendelian diseases. Nat. Neurosci. 17, 773-781, (2014). [cited by applicant]
Leffler, E. M. et al. Revisiting an old riddle: what determines genetic diversity levels within species? PLoS Biol. 10, e1001388 (2012), 9pages. [cited by applicant]
He, K, Zhang, X., Ren, S., & Sun, J. Identity mappings in deep residual networks. in 14th European Conference on Computer Vision—ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol. 9908; 630 6,15 (Springer, Ch… [cited by applicant]
Lu, Q. et al. A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data. Sci. Rep. 5, 10576 (2015), 13pgs. [cited by applicant]
Davydov, E. V. et al. Identifying a high fraction of the human genome to be under selective constraint using Gerp++. PLoS Comput. Biol. 6, Dec. 2, 2010, 13 pages. [cited by applicant]
Angermueller, et. al., “Accurate Prediction of Single Cell DNA Methylation States Using Deep Learning”, Apr. 11, 2017, 13pgs. [cited by applicant]
Ching, et. al., “Opportunities and Obstacles for Deep Learning in Biology and Medicine”, Jan. 19, 2018, 123pgs. [cited by applicant]
Ching, et. al., “Opportunities and Obstacles for Deep Learning in Biology and Medicine”, May 26, 2017, 47pgs. [cited by applicant]
Gu et. al., Recent Advances in Convolutional Neural Networks, dated Jan. 5, 2017, 37 pages. [cited by applicant]
Leung, et. al., “Inference of the Human Polyadenylation Code”, Apr. 27, 2017, 13pgs. [cited by applicant]
Leung, et. al., “Machine Learning in Genomic Medicine”, Jan. 1, 2016, 22pgs. [cited by applicant]
Schwarz, J. M., Rodelsperger, C., Schuelke, M. & Seelow, D. MutationTaster evaluates disease-causing potential of sequence alterations. Nat. Methods 7, 575-576 (2010). [cited by applicant]
Bell, C. J. et al. Comprehensive carrier testing for severe childhood recessive diseases by next generation sequencing. Sci. Transl. Med. 3, Jan. 12, 2011, 28 pages. [cited by applicant]
Hefferman, R. et al. Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning. Sci. Rep. 5, 11476 (2015) 11 pages. [cited by applicant]
Harpak, A., Bhaskar, A., & Pritchard, J. K. Mutation rate variation is a primary determinant of the distribution of allele frequencies in humans. PLoS Genet. Dec. 15, 2016, 22pgs. [cited by applicant]
Angermueller, Christof, et. al., Deep learning for computational biology, Molecular Systems Biology, dated Jun. 6, 2016, 16 pages. [cited by applicant]
Ioannidis, Nilah M., et al., “REVEL—An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants”, Oct. 5, 2016, 9 pages. [cited by applicant]
Quang Daniel, et. al., “DANN—a deep learning approach for annotating the pathogenicity of genetic variants”, Oct. 22, 2014, 3 pages. [cited by applicant]
Xiong, et. al., “The human splicing code reveals new insights into the genetic determinants of disease”, Jan. 9, 2015, 20pgs. [cited by applicant]
Yue, et. al., “Deep Learning for Genomics—A Concise Overview from internet”, May 8, 2018, 40pgs. [cited by applicant]
Yuen, et. al., “Genome wide characteristics of de novo mutations in autism”, Jun. 1, 2016, 10pgs. [cited by applicant]
Libbrecht, et. al., “Machine learning in genetics and genomics”, Jan. 2, 2017, 30pgs. [cited by applicant]
Min, et. al., “Deep Learning in Bioinformatics”, Jul. 25, 2016, 19 pgs. [cited by applicant]
Chen, Kathleen M., et. al., “Selene—a PyTorch based deep learning library for sequence level data”, Oct. 10, 2018, 15pages. [cited by applicant]
Li, et. al., “FoldingZero—Protein Folding from Scratch in Hydrophobic Polar Model”, Dec. 3, 2018, 10 pages. [cited by applicant]
Rentzsch, et. al.,_“CADD—predicting the deleteriousness of variants throughout the human genome”, Oct. 11, 2018, 9 pages. [cited by applicant]
Zou, etal, “A primer on deep learning in genomics”, Nov. 26, 2018, 7pages. [cited by applicant]
Wei et al, The Role of Balanced Training and Testing Data Sets for Binary Classifiers in Bioinformatics dated Jul. 9, 2013 12 pages. [cited by applicant]
Duggirala, Ravindranath, et.al., “Genome Mapping and Genomics in Human and Non Human Primate”, 2015, 306pgs. [cited by applicant]
Brookes, Anthony J., “The essence of SNPs”, 1999, pp. 177-186. [cited by applicant]
UniProtKB P04217 A1BG Human [retrieved on Mar. 13, 2019 from (www.uniprot.org/uniprot/P04217), 12pages. [cited by applicant]
Bahar, Protein Actions Principles and Modeling, Chapter 7, 2017 pp. 165-166. [cited by applicant]
Dunbrack, Roland L., Re Question about your Paper titled “The Role of Balanced Training and Testing Data Sets for Binary Classifiers in Bioinformatics”, Message to Sikander Mohammed Khan, Feb. 3, 2019, E-mailm, 3pgs. [cited by applicant]
DbSNP rs2241788 [Retrieved on Mar. 13, 2019], Retrieved from the Internet<www.ncbi.nlm.nih.gov/snp/rs2241788>, 5 pages. [cited by applicant]
Wei, et. al., “Prediction of phenotypes of missense mutations in human proteins from biological assemblies”, Feb. 2013, 28 pages. [cited by applicant]
Zhang, Jun, and Bin Liu. “PSFM-DBT—identifying DNA-binding proteins by combing position specific frequency matrix and distance-bigram transformation.” International journal of molecular sciences 18.9 (2017) 1856. [cited by applicant]
Gao, Tingting, et al. “Identifying translation initiation sites in prokaryotes using support vector machine.” Journal of theoretical biology 262.4 (2010) 644-649. (Year 2010). [cited by applicant]
Bi, Yingtao, et al. “Tree-based position weight matrix approach to model transcription factor binding site profiles.” PloS one6.9 (2011) e24210. [cited by applicant]
Korhonen, Janne H., et al. “Fast motif matching revisited—high-order PWMs, SNPs and indels.” Bioinformatics 33.4 (2016) 514-521. [cited by applicant]
Wong, Sebastien C., et al. “Understanding data augmentation for classification—when to warp?.” 2016 international conference on digital image computing—techniques and applications (DICTA). IEEE, 2016. [cited by applicant]
Chang, Chia-Yun, et al. “Oversampling to overcome overfitting—exploring the relationship between data set composition, molecular descriptors, and predictive modeling methods.” Journal of chemical information and modelin… [cited by applicant]
Li, Gangmin, and Bei Yao. “Classification of Genetic Mutations for Cancer Treatment with Machine Learning Approaches.” International Journal of Design, Analysis and Tools for Integrated Circuits and Systems 7.1 (2018) p… [cited by applicant]
Martin-Navarro, Antonio, et al. “Machine learning classifier for identification of damaging missense mutations exclusive to human mitochondrial DNA-encoded polypeptides.” BMC bioinformatics 18.1 (2017) p. 158. [cited by applicant]
Krizhevsky, Alex, et al, ImageNet Classification with Deep Convolutional Neural Networks, 2012, 9 Pages. [cited by applicant]
Geeks for Geeks, “Underfitting and Overfilling in Machine Learning”, [retrieved on Aug. 26, 2019]. Retrieved from the Internet <www.geeksforgeeks.org/underfitting-and-overfitting-in-machine- -learning/>, 2 pages. [cited by applicant]
Despois, Julien, “Memorizing is not learning!—6 tricks to prevent overfitting in machine learning”, Mar. 20, 2018, 17 pages. [cited by applicant]
Bhande, Anup What is underfitting and overfitting in machine learning and how to deal with it, Mar. 11, 2018, 10pages. [cited by applicant]
Carter et al., “Cancer-specific high-throughput annotation of somatic mutations—computational prediction of driver missense mutations,” Cancer research 69, No. 16 (2009) pp. 6660-6667. [cited by applicant]
Min, et. al., “Deep Learning in Bioinformatics”, Jun. 19, 2016, 46pgs. [cited by applicant]
Jiminez et. al., DeepSite—protein binding site predictor using 3D CNNs, dated Oct. 1, 2017, 7 pages. [cited by applicant]
Estrada, A. et al. Impending extinction crisis of the world's primates—why primates matter. Sc. Adv. 3, e1600946 (2017), 17 pages. [cited by applicant]
Sherry, S. T. et al. dbSNP—the NCBI database of genetic variation. Nucleic Acids Res. 29, 308-211 (2001). [cited by applicant]
Arpali et. al., High-throughput screening of large volumes of whole blood using structured illumination and fluoresecent on-chip imaging, Lab on a Chip, United Kingdom, Royal Society of Chemistry, Sep. 12, 2012, vol. 12… [cited by applicant]
Liu et. al., 3D Stacked Many Core Architecture for Biological Sequence Analysis Problems, 2017, Int J Parallel Prog, 45:1420-1460. [cited by applicant]
Wu et. al., FPGA-Based DNA Basecalling Hardware Acceleration, in Proc. IEEE 61st Int. Midwest Symp. Circuits Syst., Aug. 2018, pp. 1098-1101. [cited by applicant]
Wu et al., FPGA-Accelerated 3rd Generation DNA Sequencing, in IEEE Transactions on Biomedical Circuits and Systems, vol. 14, Issue 1, Feb. 2020, pp. 65-74. [cited by applicant]
Prabhakar et. al., Plasticine: A Reconfigurable Architecture for Parallel Patterns, ISCA '17, Jun. 24-28, 2017, Toronto, ON, Canada. [cited by applicant]
Lin et. al., Network in Network, in Proc. of ICLR, 2014. [cited by applicant]
Sifre, Rigid-motion Scattering for Image Classification, Ph.D. thesis, 2014. [cited by applicant]
Sifre et. al., Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination, in Proc. of CVPR, 2013. [cited by applicant]
Chollet, Xception: Deep Learning with Depthwise Separable Convolutions, in Proc. of CVPR, 2017. 8 pages. [cited by applicant]
Zhang et. al., ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices, 2017. [cited by applicant]
He et. al., Deep Residual Learning for Image Recognition, in Proc. of CVPR, 2016. [cited by applicant]
Xie et. al., Aggregated Residual Transformations for Deep Neural Networks, in Proc. of CVPR, 2017. [cited by applicant]
Howard et. al., Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications, 2017. [cited by applicant]
Sandler et. al., MobileNetV2: Inverted Residuals and Linear Bottlenecks, 2018. [cited by applicant]
Qin et. al., FD-MobileNet: Improved MobileNet with a Fast Downsampling Strategy, 2018. [cited by applicant]
Chen et. al., Rethinking atrous convolution for semantic image segmentation, 2017. [cited by applicant]
Huang et. al., Speed/accuracy trade-offs for modern convolutional detectors, 2016. [cited by applicant]
Oord et al., WAVENET: A Generative Model for Raw Audio, dated Sep. 19, 2016, 15 pages. [cited by applicant]
Arik et al., Deep Voice: Real-time Neural Text-to-Speech, dated 2017, 17 pages. [cited by applicant]
Yu et al., Multi-Scale Context Aggregation by Dilated Convolutions, ICLR 2016, dated Apr. 30, 2016, 13 pages. [cited by applicant]
He et. al., Deep Residual Learning for Image Recognition, 2015. [cited by applicant]
Srivastava et al., Highway Networks, dated 2015, 6 pages. [cited by applicant]
Huang et al., Densely Connected Convolutional Networks, dated Aug. 17, 2017, 9 pages. [cited by applicant]
Szegedy et al., Going Deeper with Convolutions, dated 2014, 12 pages. [cited by applicant]
Ioffe et al., Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift, dated 2015, 11 pages. [cited by applicant]
Wolterink et al., Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease, dated 2017, 9 pages. [cited by applicant]
Piqueras, Autoregressive Model Based on a Deep Convolutional Neural Network for Audio Generation, Tampere University of Technology, dated 2016, 58 pages. [cited by applicant]
Wu, Introduction to Convolutional Neural Networks, Nanjing University, dated 2017, 31 pages. [cited by applicant]
scikit-image/peak.py at master, Github, retrieved on Jun. 8, 2021, 10 pages, Retrieved from the internet <URL: https://github.com/scikit-image/scikit-image/blob/main/skimage/feature/peak.py>. [cited by applicant]
3.3.9.11.Watershed and random walker for segmentation, Scipy lecture notes, 2 pages. [retrieved on Jun. 8, 2021] Retrieved from the internet <URL: http:scipy-lectures.org/packages/scikit-image/auto_examples/plot_segment… [cited by applicant]
Mordvintsev et al., Image Segmentation with Watershed Algorithm, Revision 43532856, 2013, 6 pages. [retrieved on Jun. 8, 2021] Retrieved from the Internet <URL: https://opencv-python-tutroals.readthedocs.io/en/latest/py… [cited by applicant]
Mzur, Watershed.py, Github, 3 pages. [retrieved on Jun. 8, 2021] Retrieved from the internet <URL: https://github.com/mzur/watershed/blob/master/Watershed.py>. [cited by applicant]
Thakur et al., A Survey of Image Segmentation Techniques, International Journal of Research in Computer Applications and Robotics, vol. 2, Issue 4, Apr. 2014, p. 158-165. [cited by applicant]
Long et. al., Fully Convolutional Networks for Semantic Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, Issue 4, Apr. 1, 2017, 12 pages. [cited by applicant]
Ronneberger et. al., U-net: Convolutional networks for biomedical image segmentation, in International Conference on Medical Image computing and computer assisted intervention, May 18, 2015, 8 pages. [cited by applicant]
Xie et al., Microscopy cell counting and detection with fully convolutional regression networks, Computer methods in biomechanics and biomedical engineering, Imaging and Visualization, 6(3), pp. 283-292, 2018. [cited by applicant]
Xie, Y., et. al., Beyond classification: structured regression for robust cell detection using convolutional neural network. International conference on medical image computing and computer assisted intervention, Oct. 2… [cited by applicant]
Snuverink, Deep Learning for Pixelwise Classification of Hyperspectral Images, Master of Science Thesis, Delft University of Technology, Nov. 23, 2017, 128 pages. [cited by applicant]
Shevchenko, Keras weighted categorical_crossentropy, Github, [retrieved on Jun. 12, 2021], Retrieved from the internet <URL: https://gist.github.com/skeeet/cad06d584548fb45eece1d4e28cfa98b >, 2 pages. [cited by applicant]
Assem, Predicting periodic and chaotic signals using Wavenets, Master of Science thesis, Delft University of Technology, Aug. 18, 2017, pp. 3-38. [cited by applicant]
Goodfellow et al., Chapter 9—Convolutional Networks, Deep Learning, MIT Press, dated 2016, 41 pages. [cited by applicant]
Albrecht et. al., Deep learning for single-molecule science, Nanotechnology (28), dated 2017, 423001, 11 pages. [cited by applicant]
MISEQ: Imaging and Base Calling: Illumina, Inc. Online Training Course, dated Jan. 1, 2013 [retrieved on Jul. 13, 2020] , Retrieved from <URL: https://support.illumina.com/training.html >, 13 pages. [cited by applicant]
MiSEQ: Imaging and Base Calling Script, retrieved on [Jun. 14, 2021], Retrieved from the internet <URL: https://support.illumina.com/content/dam/illumina-support/courses/MiSeq_Imaging_and_Base_Calling/story_content/exte… [cited by applicant]
Zhao et. al., Object detection with Deep Learning: A Review, dated Jul. 15, 2018, 22 pages. [cited by applicant]
Lee et. al., Fast Object Localization Using a CNN Feature Map Based Multi-Scale Search, dated Apr. 12, 2016, 16 pages. [cited by applicant]
Misiunas et. al., QuipuNet: convolutional neural network for single-molecule nanopore sensing, dated May 30, 2018, 7 pages. [cited by applicant]
Boza et. al., Deep Recurrent Neural Networks for Base Calling in MinION Nanopore Reads, dated Mar. 30, 2016, 12 pages. [cited by applicant]
Kao et. al., BayesCall: A model-based base-calling algorithm for high-throughput short-read sequencing, Genome Research (19), pp. 1884-1895, dated 2009. [cited by applicant]
Rang et. al., From squiggle to basepair: computational approaches for improving nanopore sequencing read accuracy, Genome Biology 2018, (19), 30. [cited by applicant]
Wang et. al., An adaptive decorrelation method removes Illumina DNA base-calling errors caused by crosstalk between adjacent clusters, Scientific Reports, published Feb. 20, 2017, 11 pages. [cited by applicant]
Cacho et. al., A comparison of Base Calling Algorithms for Illumina Sequencing Technology, dated Oct. 5, 2015, Briefings in Bioinformatics 2016 (17), 786-795. [cited by applicant]
Luo et. al., G-softmax: Improving Intra-class Compactness and Inter-class Separability of Features, dated Apr. 8, 2019, 15 pages. [cited by applicant]
Luo et. al., A multi-task convolutional deep neural network for variant calling in single molecule sequencing, Nature Communications (10), No. 1, dated Mar. 1, 2019. [cited by applicant]
Kingma et. al., Adam: A method for Stochastic Optimization, ICLR 2015, dated Jul. 23, 2015. [cited by applicant]
Luo et. al., Skyhawk: An Artificial Neural Network-based discriminator for reviewing clinically significant genomic variants, dated Jan. 28, 2019, 8 pages. [cited by applicant]
MiSEQ: Imaging and Base Calling: Illumina, Inc. Online Training Course, colored version, [retrieved on Oct. 11, 2020], Retrieved from <URL: https://support.illumina.com/training.html >, 9 pages. [cited by applicant]
Kircher et. al., Improved base-calling for the Illumina Genome Analyzer using Machine Learning Strategies, Genome Biology, published Aug. 14, 2009, 9 pages. [cited by applicant]
Smith et. al., Barcoding and demultiplexing Oxford nanopore native RNA sequencing reads with deep residual learning, bioRxiv, dated Dec. 5, 2019, 18 pages. [cited by applicant]
Aggarwal, Neural Networks and Deep Learning: A Textbook, Springer, dated Aug. 26, 2018, 512 pages. [cited by applicant]